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It systematically questions the idea across dimensions (user personas, success metrics, constraints, dependencies) and produces a structured clarification document that serves as the foundation for all downstream stages. This stage acts as a requirements elicitation engine, converting narrative descriptions into enumerated, unambiguous statements.","intents":["Identify hidden assumptions and ambiguities in a product idea before engineering starts","Define clear scope boundaries and out-of-scope items","Extract implicit user personas and success criteria from vague descriptions","Create a shared understanding document that prevents scope creep during implementation"],"best_for":["Product managers and founders with ideas but unclear scope","Teams starting projects without formal requirements gathering","Solo developers who need to think through ideas systematically before coding"],"limitations":["Relies on LLM's ability to infer missing context — may miss domain-specific nuances","No validation that clarifications are actually correct or complete","Cannot handle ideas that are fundamentally incoherent or contradictory without human intervention","Output quality varies significantly based on LLM model capability"],"requires":["Access to an LLM with reasoning capability (Claude 3+, GPT-4, or equivalent)","Initial product idea or problem statement in natural language"],"input_types":["natural language product idea","problem statement or feature request"],"output_types":["structured clarification document with enumerated assumptions, scope, personas, success metrics"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nurettincoban--ai-prd-workflow__cap_2","uri":"capability://planning.reasoning.technical.feasibility.and.architecture.analysis","name":"technical feasibility and architecture analysis","description":"Takes the clarified requirements and performs a structured technical analysis to identify architectural patterns, technology choices, potential bottlenecks, and implementation risks. This stage synthesizes the clarification output with technical knowledge to produce a feasibility assessment and high-level architecture recommendation. It operates as a technical advisor layer, evaluating trade-offs between different implementation approaches and flagging risks early.","intents":["Assess technical feasibility of a product idea before committing engineering resources","Identify architectural patterns and technology stacks suitable for the requirements","Flag technical risks, scalability concerns, and integration challenges early","Generate architecture recommendations that inform the detailed specification stage"],"best_for":["Technical leads evaluating new product ideas for feasibility","Teams deciding between different architectural approaches","Solo developers who need to validate technical assumptions before building"],"limitations":["Analysis is generic and may not account for team-specific constraints (skills, infrastructure, budget)","Cannot validate recommendations against actual performance benchmarks or real-world constraints","Relies on LLM's knowledge cutoff — may miss recent technology developments or deprecated approaches","No access to actual codebase or system metrics to inform analysis"],"requires":["Clarified requirements from the previous pipeline stage","LLM with strong technical knowledge (Claude 3+, GPT-4, or equivalent)"],"input_types":["structured clarification document from stage 1","requirements breakdown with personas, metrics, scope"],"output_types":["feasibility assessment (go/no-go recommendation)","architecture recommendation with technology choices","risk assessment and mitigation strategies"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nurettincoban--ai-prd-workflow__cap_3","uri":"capability://text.generation.language.structured.rfc.and.specification.generation","name":"structured rfc and specification generation","description":"Synthesizes outputs from clarification and technical analysis stages to generate a complete, structured RFC document with detailed specifications, acceptance criteria, and implementation guidelines. This stage uses a template-driven approach where the prompt includes a specification schema (sections for overview, requirements, architecture, acceptance criteria, timeline, dependencies) and fills each section with content derived from earlier stages. The output is formatted for direct consumption by developers and code generation tools.","intents":["Generate a complete RFC document ready for engineering review and approval","Create detailed acceptance criteria that developers can use to validate implementation","Produce implementation guidelines and architecture diagrams in text form","Generate a specification that can be fed directly into code generation tools (Claude Code, Cursor, etc.)"],"best_for":["Teams that need formal RFC documentation before starting development","Organizations using code generation tools that require detailed specifications","Projects where specification quality directly impacts implementation speed"],"limitations":["Generated RFC may lack domain-specific details or edge cases not captured in earlier stages","Acceptance criteria are generated heuristically and may not be comprehensive or testable","No validation that the RFC is internally consistent or technically sound","Output length and detail depend on LLM context window and generation limits"],"requires":["Outputs from clarification and technical analysis stages","LLM with strong writing and structured output capability"],"input_types":["clarification document from stage 1","technical analysis and architecture recommendations from stage 2"],"output_types":["complete RFC document in markdown or structured format","detailed specification with sections for overview, requirements, architecture, acceptance criteria","implementation timeline and dependency list"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nurettincoban--ai-prd-workflow__cap_4","uri":"capability://planning.reasoning.implementation.task.decomposition.and.timeline.generation","name":"implementation task decomposition and timeline generation","description":"Breaks down the RFC into granular, sequenced implementation tasks with estimated effort, dependencies, and success criteria. This stage takes the detailed specification and produces a task list that developers can immediately begin working from, including task ordering based on dependencies, effort estimates, and clear acceptance criteria for each task. It operates as a project planning layer, converting specification into actionable work items.","intents":["Convert a high-level RFC into a concrete task list that developers can start working on immediately","Estimate effort and timeline for implementation based on specification complexity","Identify task dependencies and critical path for parallel work","Generate acceptance criteria for each task to enable clear definition of done"],"best_for":["Teams that need to translate specifications into sprint-ready tasks","Solo developers who need a structured implementation plan","Projects where task clarity directly impacts development velocity"],"limitations":["Effort estimates are heuristic and may not account for team skill level, infrastructure maturity, or unknown unknowns","Task decomposition may be too granular or too coarse depending on team size and project complexity","No feedback mechanism to validate task estimates against actual implementation time","Cannot account for team-specific constraints (availability, skill gaps, infrastructure limitations)"],"requires":["Complete RFC and specification from stage 3","LLM with project planning and decomposition capability"],"input_types":["complete RFC document with detailed specifications","architecture recommendations and technical analysis"],"output_types":["structured task list with effort estimates and dependencies","implementation timeline and critical path","task-level acceptance criteria"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nurettincoban--ai-prd-workflow__cap_5","uri":"capability://tool.use.integration.llm.agnostic.prompt.pipeline.execution","name":"llm-agnostic prompt pipeline execution","description":"Provides a shell-based execution framework that chains prompts across different LLM providers (Claude, ChatGPT, Cursor, Ollama) without requiring SDK-specific code. The pipeline uses standard input/output redirection and API calls to invoke different LLMs, storing intermediate outputs as files that feed into subsequent stages. This architecture enables users to mix and match LLM providers (e.g., use Claude for clarification, GPT-4 for analysis, Cursor for code generation) without rewriting the pipeline.","intents":["Execute a multi-stage prompt pipeline without being locked into a single LLM provider","Mix and match different LLM providers for different stages based on capability or cost","Integrate the pipeline into existing shell-based workflows and CI/CD systems","Enable reproducibility by storing intermediate outputs as versioned files"],"best_for":["Teams using multiple LLM providers and wanting to leverage each for different tasks","Developers who want to integrate the pipeline into existing shell workflows","Organizations that need reproducibility and audit trails for specification generation"],"limitations":["Requires manual API key management and shell environment setup for each LLM provider","No built-in error handling or retry logic for API failures","No orchestration of parallel stages — pipeline is strictly sequential","Intermediate outputs must be manually managed; no built-in versioning or cleanup"],"requires":["Shell environment (bash, zsh, or equivalent)","API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","curl or equivalent HTTP client for API calls","Basic understanding of shell scripting and environment variables"],"input_types":["shell scripts with embedded prompts","environment variables for API configuration"],"output_types":["intermediate files at each pipeline stage","final RFC and specification documents"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nurettincoban--ai-prd-workflow__cap_6","uri":"capability://planning.reasoning.context.aware.prompt.chaining.with.output.inheritance","name":"context-aware prompt chaining with output inheritance","description":"Implements a prompt chaining pattern where each stage's output is automatically included as context in the next stage's prompt, creating a dependency graph of prompts. The pipeline uses file-based context passing where outputs from stage N become inputs to stage N+1, enabling later stages to reference and build upon earlier structured outputs. This pattern reduces hallucination and improves coherence by ensuring each stage operates on concrete, structured context rather than abstract requirements.","intents":["Reduce hallucination and inconsistency by ensuring each stage operates on concrete earlier outputs","Enable later stages to reference specific decisions and trade-offs made in earlier stages","Create a traceable chain of reasoning from vague idea to implementation tasks","Improve output quality by providing rich context rather than starting from scratch at each stage"],"best_for":["Projects where specification consistency and traceability are critical","Teams that need to understand the reasoning behind architectural decisions","Workflows where later stages must reference and build upon earlier decisions"],"limitations":["Context window limitations may prevent including full earlier outputs in later prompts","Errors or hallucinations in early stages propagate to later stages","No mechanism to backtrack or revise earlier stages if later stages identify issues","File-based context passing adds latency and requires disk I/O"],"requires":["Structured outputs from earlier stages in the pipeline","LLM with sufficient context window to include earlier outputs"],"input_types":["structured outputs from previous pipeline stages"],"output_types":["refined outputs that reference and build upon earlier stages"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nurettincoban--ai-prd-workflow__cap_7","uri":"capability://automation.workflow.vibe.coding.workflow.integration.and.formalization","name":"vibe-coding workflow integration and formalization","description":"Provides a structured checkpoint system that formalizes 'vibe coding' workflows (rapid prototyping with AI assistants) by injecting specification and planning stages between ideation and implementation. The pipeline acts as a formalization layer that captures the implicit decisions made during vibe coding and converts them into explicit, documented specifications. This enables teams to maintain the speed of vibe coding while adding rigor and traceability.","intents":["Formalize vibe coding workflows by adding specification checkpoints without slowing down iteration","Capture implicit architectural decisions made during rapid prototyping","Create documentation and specifications from vibe-coded prototypes","Enable team collaboration on vibe-coded projects by making implicit decisions explicit"],"best_for":["Teams using AI code assistants (Claude Code, Cursor) for rapid prototyping","Solo developers who want to maintain vibe coding speed while adding rigor","Organizations transitioning from ad-hoc prototyping to more structured development"],"limitations":["Specification generation from existing code may miss implicit design decisions","Adding specification stages slows down iteration cycles compared to pure vibe coding","Specifications generated after implementation may not match actual code behavior","Requires discipline to use the pipeline consistently rather than reverting to pure vibe coding"],"requires":["Access to AI code assistants (Claude Code, Cursor, or equivalent)","Willingness to add specification stages to the development workflow"],"input_types":["vague product idea or feature request","existing prototype code (optional)"],"output_types":["formalized specification and RFC","implementation tasks and timeline"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"low","permissions":["Access to any LLM API or chat interface (Claude, ChatGPT, Cursor, Ollama, etc.)","Shell environment to execute pipeline scripts","Basic understanding of product requirements and technical specification formats","Access to an LLM with reasoning capability (Claude 3+, GPT-4, or equivalent)","Initial product idea or problem statement in natural language","Clarified requirements from the previous pipeline stage","LLM with strong technical knowledge (Claude 3+, GPT-4, or equivalent)","Outputs from clarification and technical analysis stages","LLM with strong writing and structured output capability","Complete RFC and specification from stage 3"],"failure_modes":["No built-in persistence — outputs must be manually saved or piped to external storage","Quality depends entirely on input idea clarity; garbage-in-garbage-out for extremely vague concepts","No feedback loops or iterative refinement within the pipeline — linear progression only","Requires manual invocation of each stage; no orchestration engine for batch processing","No validation that generated RFC is actually implementable without human review","Relies on LLM's ability to infer missing context — may miss domain-specific nuances","No validation that clarifications are actually correct or complete","Cannot handle ideas that are fundamentally incoherent or contradictory without human intervention","Output quality varies significantly based on LLM model capability","Analysis is generic and may not account for team-specific constraints (skills, infrastructure, budget)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1625122366132473,"quality":0.41,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:59:55.150Z","last_commit":"2026-03-21T10:51:17Z"},"community":{"stars":250,"forks":29,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nurettincoban--ai-prd-workflow","compare_url":"https://unfragile.ai/compare?artifact=nurettincoban--ai-prd-workflow"}},"signature":"0G58HwyVRM34W+/cZFP1JNoq4EFAVOanAAzQuBQuDmGZ5fivlVOIIgR9xJCPEfN9zi8KukOP9ufj+MOFw73TDg==","signedAt":"2026-06-20T10:51:23.090Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nurettincoban--ai-prd-workflow","artifact":"https://unfragile.ai/nurettincoban--ai-prd-workflow","verify":"https://unfragile.ai/api/v1/verify?slug=nurettincoban--ai-prd-workflow","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}